Robust model predictive control using tubes

By W. Langson, Ioannis Chryssochoos, Sasa V. Rakovic, David Q. Mayne. at - Automatisierungstechnik, 2004

738 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/00a8e69ce77486980771ce5972eae16bffa5c893

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In Control and Systems Engineering

In Molecular Biology

Citations in the corpus

Robust Constrained Control of Uncertain Macroscopic Fundamental Diagram Networks (2015, background): “However, most reported robust MPC in the literature, see e.g. Kothare et al. (1996); Langson et al. (2004), present great conservatives and on-line computational burden.”
Provably safe and robust learning-based model predictive control (2011, positive): “…by choosing inputs that minimize a cost subject to the dynamics of a learned model that is updated using statistics, while ensuring safety and stability by using theory from robust MPC [19,21,42,44] to check whether these same inputs keep a nominal model stable when it is subject to uncertainty.”
Invariant approximations of the minimal robust positively Invariant set (2005, methodology): “The mRPI set is also a suitable target set in robust time-optimal control [10]–[13] and plays an integral part in a novel robust predictive control method, recently proposed in [14].”
Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, methodology): “125,181 One such approach using abstract obstacle information is the VisBug class of algorithms, which navigates toward a visible edge of an obstacle inside the detection range (see, e.g., [140, 160, 197]).”
Optimization over state feedback policies for robust control with constraints (2006, background): “An interesting recent proposal in the predictive control literature is to dene a polytopic tube over the control horizon and compute control inputs only at the vertices of this tube [ 37 ].”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, methodology): “To address the shortcomings of min–max OCPs, tube-based MPC has recently been developed [12]–[15].”
Postface to “ Model Predictive Control : Theory and Design ” (2012, result): “Thus Limon, Alamo, Raimondo, de la Peña, Bravo, and Camacho (2008) present the theory of input-to-state stability as a unifying framework for robust MPC, generalizes the tubebased MPC described in (Langson et al., 2004), and extends existing results on min-max MPC.”
Set-Theoretic Approaches in Analysis, Estimation and Control of Nonlinear Systems (2015, methodology): “, Bertsekas, 2007; Dadhe and Engell, 2008; Goulart and Kerrigan, 2006), our focus here is on tube-based MPC (Langson et al., 2004), whereby the predicted trajectories used in traditional MPC are replaced by robust forward invariant tubes; that is, tubes enclosing all the response trajectories for a ”
Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking (2016, background): “Tube MPC is a robust constrained algorithm that accounts for model uncertainty when considering constraints (Langson et al., 2004; Mayne et al., 2011).”